Space constraints mean we could only provide the briefest summaries of some interesting and important research in the fourth issue of the SEBP newsletter. These do not do the studies justice, but have hopefully piqued your curiosity enough to bring you here.
Predicting Knife Crime: An Individual and Network-Based Approach, by DCI Lewis Prescott-Mayling
In this study, published in the Thames Valley Police Journal, DCI Lewis Mayling, from Thames Valley Police's Violence Reduction Unit, set out to find whether machine learning can be used to predict future knife crime perpetrators. These predictions would be generating using administrative information already known to the police at the time of a reported crime. Feature (or predictor) variables were created using over 54,000 unique crimes, involving over 26,000 unique perpetrators, reported to Thames Valley Police between between 29th April 2014 and 14th June 2019. These variables were created about the perpetrator as an individual and about their co-offending network. For example, individual-based feature variables include: total number of times they have been a perpetrator of violent crime, or how recently they perpetrated knife crime. Using social network methodology, additional network-based feature variables regarding co-offenders of the perpetrator were generated. Such feature variables include numbers of previous knife crime perpetration by co-offenders. A random forest model was generated to predict if a perpetrator in any reported crime would commit a knife crime within one year. There were over 64,700 events generating a prediction, seven per cent of which contain a perpetrator who does commit a knife crime within that time. The model correctly identified half of all perpetrators who do commit a knife crime within one year. However, of all those predicted to commit a knife crime, just 1 in 5 actually do. The study concluded an individual’s co-offender network having a history of ‘violence’, ‘drug trafficking’ and ‘knife crime’ increases the likelihood of the individual committing knife crime. However, individual-based features, such as ‘age of onset of perpetration’ and a history of ‘missing person reports’, are more important predictors.
Tracking Procedural Justice in Processing Detainees: Coding Evidence from CCTV Cameras in Three Police Custody Suites, by Catherine Susan Firman and Justice Tankebe
This tracking study, published in the Cambridge Journal of Evidence Based Policing, aims to discover how closely custody suite encounters between detainees and custody suite officers match the procedural standards for decision makers treating people who are subject to their authority. It also set out to discover how procedural justice relates to use of force and detainee compliance, and what variations there are across detainees, CSOs and custody suites. A random sample of 150 encounters was selected for analysis from arrest records for July, August, and September 2020 generated by three custody suites in the East of England. Encounters between CSOs and detainees at the booking-in stage as captured on pre-recorded CCTV were coded into four elements of procedural justice: voice, trustworthy motives, impartiality, and respect. Non-verbal communications and dialogue were also examined. The study found, overall, custody suite officers demonstrated high levels of respect and neutrality in dealing with detainees. However, they showed relatively less care for the wellbeing of the detainees and did not offer them enough opportunities to ‘tell their side of the story’ (‘voice’). Further analysis revealed statistically significant variations across the three custody suites in the level of opportunities offered to detainees to have an input in discussing the decision-making. They also found evidence that as length of service as police officers and in custody roles increased, the observed level of expression of ‘trustworthy motives’ displayed decreased. Finally, detainee compliance with officers was greater when respect and care for the wellbeing of detainees were more pronounced. Such evidence can support more targeted training to improve the delivery of procedural justice, and enhance public confidence in policing.
The Effects of Body-Worn Cameras on Self-Initiated Police Encounters by Richard Bennett, Brad Bartholomew, Sandra K. Baxter, Holly Champagne, Eric R. Schuler
Beginning in the summer of 2014, a series of controversial deaths in the US, often involving white officers and unarmed minority men, contributed to the growing debate about the need for increased transparency and accountability in policing. Central to the debate has been the call for more body-worn cameras (BWCs), a widely adopted technology that promises numerous benefits through real-time recording of police-public encounters. However, critics of this technology claim that increased surveillance of their behavior may lead to de-policing where officers choose to reduce the quality and/or quantity of their self-initiated police work. This study, published in Police Quarterly, set out to discover whether wearing BWCs had any influence on police discretionary activity. Measures of the two main types of self-initiated police behavior, foot patrols and traffic stops, were collected during a lengthy pilot program based on a quasi-experimental design. The data were gathered in three large, diverse districts of an urban police organization and analyzed using the Stata itsa statistical program. This study found no significant differences over three time periods in the number of self-initiated foot patrols or traffic stops made by officers assigned cameras compared to officers that were not. These null findings support the conclusion that police officers do not reduce their likelihood of stopping drivers or patrolling neighborhoods on foot when they wear body-worn cameras.